
import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowerCamelCase__						=				get_logger(__name__)


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=0			)		->					Any:

		"""simple docstring"""






		os.makedirs(a_    ,	exist_ok=a_			)
		with FSDP.state_dict_type(
		    a_    ,	fsdp_plugin.state_dict_type    ,	fsdp_plugin.state_dict_config    ,	fsdp_plugin.optim_state_dict_config			):
				_UpperCamelCase         =						model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						_UpperCamelCase         =						F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						_UpperCamelCase         =						os.path.join(a_    ,	a_			)
						if accelerator.process_index == 0:
								logger.info(F'Saving model to {output_model_file}'			)
								torch.save(a_    ,	a_			)
								logger.info(F'Model saved to {output_model_file}'			)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						_UpperCamelCase         =						(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						_UpperCamelCase         =						os.path.join(a_    ,	a_			)
						logger.info(F'Saving model to {output_model_file}'			)
						torch.save(a_    ,	a_			)
						logger.info(F'Model saved to {output_model_file}'			)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						_UpperCamelCase         =						os.path.join(a_    ,	F'{MODEL_NAME}_{model_index}'			)
						os.makedirs(a_    ,	exist_ok=a_			)
						logger.info(F'Saving model to {ckpt_dir}'			)
						_UpperCamelCase         =						{"model": state_dict}

						dist_cp.save_state_dict(
						    state_dict=a_    ,	storage_writer=dist_cp.FileSystemWriter(a_			)    ,	planner=DefaultSavePlanner()    ,	)
						logger.info(F'Model saved to {ckpt_dir}'			)


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=0			)		->					Any:

		"""simple docstring"""






		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a_    ,	fsdp_plugin.state_dict_type    ,	fsdp_plugin.state_dict_config    ,	fsdp_plugin.optim_state_dict_config			):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(a_			) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    """Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
										    """initializing FSDP object"""			)
								return
						_UpperCamelCase         =						F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						_UpperCamelCase         =						os.path.join(a_    ,	a_			)
						logger.info(F'Loading model from {input_model_file}'			)
						_UpperCamelCase         =						torch.load(a_			)
						logger.info(F'Model loaded from {input_model_file}'			)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						_UpperCamelCase         =						(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						_UpperCamelCase         =						os.path.join(a_    ,	a_			)
						logger.info(F'Loading model from {input_model_file}'			)
						_UpperCamelCase         =						torch.load(a_			)
						logger.info(F'Model loaded from {input_model_file}'			)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						_UpperCamelCase         =						(
						    os.path.join(a_    ,	F'{MODEL_NAME}_{model_index}'			)
						    if F'{MODEL_NAME}' not in input_dir
						    else input_dir
						)
						logger.info(F'Loading model from {ckpt_dir}'			)
						_UpperCamelCase         =						{"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=a_    ,	storage_reader=dist_cp.FileSystemReader(a_			)    ,	planner=DefaultLoadPlanner()    ,	)
						_UpperCamelCase         =						state_dict["model"]
						logger.info(F'Model loaded from {ckpt_dir}'			)
				model.load_state_dict(a_			)


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=0			)		->					List[str]:

		"""simple docstring"""






		os.makedirs(a_    ,	exist_ok=a_			)
		with FSDP.state_dict_type(
		    a_    ,	fsdp_plugin.state_dict_type    ,	fsdp_plugin.state_dict_config    ,	fsdp_plugin.optim_state_dict_config			):
				_UpperCamelCase         =						FSDP.optim_state_dict(a_    ,	a_			)
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								_UpperCamelCase         =						(
								    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
								)
								_UpperCamelCase         =						os.path.join(a_    ,	a_			)
								logger.info(F'Saving Optimizer state to {output_optimizer_file}'			)
								torch.save(a_    ,	a_			)
								logger.info(F'Optimizer state saved in {output_optimizer_file}'			)
				else:
						_UpperCamelCase         =						os.path.join(a_    ,	F'{OPTIMIZER_NAME}_{optimizer_index}'			)
						os.makedirs(a_    ,	exist_ok=a_			)
						logger.info(F'Saving Optimizer state to {ckpt_dir}'			)
						dist_cp.save_state_dict(
						    state_dict={"""optimizer""": optim_state}    ,	storage_writer=dist_cp.FileSystemWriter(a_			)    ,	planner=DefaultSavePlanner()    ,	)
						logger.info(F'Optimizer state saved in {ckpt_dir}'			)







def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=0			)		->					List[Any]:

		"""simple docstring"""






		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a_    ,	fsdp_plugin.state_dict_type    ,	fsdp_plugin.state_dict_config    ,	fsdp_plugin.optim_state_dict_config			):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						_UpperCamelCase         =						None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						_UpperCamelCase         =						(
						    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
						)
						_UpperCamelCase         =						os.path.join(a_    ,	a_			)
						logger.info(F'Loading Optimizer state from {input_optimizer_file}'			)
						_UpperCamelCase         =						torch.load(a_			)
						logger.info(F'Optimizer state loaded from {input_optimizer_file}'			)
				else:
						_UpperCamelCase         =						(
						    os.path.join(a_    ,	F'{OPTIMIZER_NAME}_{optimizer_index}'			)
						    if F'{OPTIMIZER_NAME}' not in input_dir
						    else input_dir
						)
						logger.info(F'Loading Optimizer from {ckpt_dir}'			)
						_UpperCamelCase         =						load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()    ,	optimizer_key="""optimizer"""    ,	storage_reader=dist_cp.FileSystemReader(a_			)    ,	)
						_UpperCamelCase         =						optim_state["optimizer"]
						logger.info(F'Optimizer loaded from {ckpt_dir}'			)
				_UpperCamelCase         =						FSDP.optim_state_dict_to_load(a_    ,	a_    ,	a_			)
				optimizer.load_state_dict(a_			)










'''simple docstring'''






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def   __A    (				a_    :	str   ,a_    :	str   ,**a_    :	int						):
		lowerCAmelCase  :     str       						= AutoConfig.from_pretrained(a_   ,**a_						)
		lowerCAmelCase  :     Any       						= AutoModelForSeqaSeqLM.from_config(a_						)
		model.save_pretrained(a_						)
		AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)
		return model


if __name__ == "__main__":
					fire.Fire(save_randomly_initialized_version)



